A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network

Bao, Wenkang; Liu, Haidong; Sun, Yao; Zheng, Yuejiu

doi:10.3390/batteries8120289

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…21 Data-driven methods for SOC estimation usually make use of neural networks, 22 support vector machines, deep learning, and other data-driven techniques. 23,24 They train models to directly map the relationship between various parameters like voltage, current, and temperature measured during battery operation to the SOC, 25 without the need for mathematical battery models. Data-driven SOC estimation methods typically offer high accuracy and strong adaptability, 26 making them a preferred choice among researchers in the field of lithium-ion battery SOC estimation.…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly-proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

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Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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“…This section establishes the relation between the feature set and the SOH by employing diverse ML methods to validate the reliability of the constructed features on the downgraded data. To make the proposed method deployable in the battery management system (BMS), the dominant ML methods such as SVRs [41][42][43], BP [44][45][46], and RF [47,48] are employed. Due to the strong correlation between the proposed features and capacity, a high SOH estimation accuracy can be expected.…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Wang,

Zhang,

Meng

et al. 2024

Batteries

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Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

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“…Therefore, the OCV method is not applicable in devices where prolonged operational interruptions are not feasible. Nevertheless, research continues to explore ways to reduce the measurement time for the OCV method, leveraging its ease of measurement and computational simplicity [10,11]. EIS estimates SOC by analyzing battery impedance obtained through frequency response.…”

mentioning

confidence: 99%

Voltage relaxation curve‐based state of charge estimation method with reduced measurement time

Lee,

Lee

2023

Electronics Letters

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This paper proposes a method for estimating a battery's State of Charge using voltage relaxation curves. This method offers advantages over the conventional State of Charge estimation methods by reducing the measurement time of the Open Circuit Voltage method and eliminating the need for specialized equipment required in Electrochemical Impedance Spectroscopy. The influence of battery parameters on voltage relaxation curve was analyzed through simulations, and based on this relationship, battery parameters were obtained by fitting the voltage relaxation curve. The State of Charge was estimated using the battery parameter look‐up table for each State of Charge prepared in advance, and the measurement time could be shortened through graph fitting for the initial section of the voltage relaxation curve. The proposed method allows for a reduction in measurement time compared to the traditional Open Circuit Voltage method, while achieving State of Charge estimation with an error rate of less than 5.00 percentage.

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A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network

Cited by 9 publications

References 37 publications

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Voltage relaxation curve‐based state of charge estimation method with reduced measurement time

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